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We present a novel Machine Learning (ML) based strategy to search for binary black hole (BBH) mergers in data from ground-based gravitational wave (GW) observatories. This is the first ML-based search that not only recovers all the compact binary coalescences (CBCs) in the first GW transients catalog (GWTC-1), but also makes a clean detection of GW151216 by only adding a new coincident ranking statistic (MLStat) to a standard analysis that was used for GWTC-1. In CBC searches, reducing contamination by terrestrial and instrumental transients, which create a loud noise background by triggering numerous false alarms, is crucial to improving the sensitivity for detecting true events. The sheer volume of data and a large number of expected detections also prompts the use of ML techniques. We perform transfer learning to train "InceptionV3", a pre-trained deep neural network, along with curriculum learning to distinguish GW signals from noisy events by analysing their continuous wavelet transform (CWT) maps. MLStat incorporates information from this ML classifier into the coincident search likelihood used by the standard PyCBC search. This leads to at least an order of magnitude improvement in the inverse false-alarm-rate (IFAR) for the previously "low significance" events GW151012, GW170729 and GW151216. We also perform the parameter estimation of GW151216 using SEOBNRv4HM_ROM. We carry out an injection study to show that MLStat brings substantial improvement to the detection sensitivity of Advanced LIGO for all compact binary coalescences. The average improvement in the sensitive volume is ~10% for low chirp masses (0.8-5 Msun), and ~30% for higher masses (5-50 Msun). This work demonstrates the immense potential and readiness of MLStat for finding new sources in current data and the possibility of its adaptation in similar searches.